AI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
LLMs
Practice saying the answer clearly, then explain the insurance example and the trade-off. A strong answer identifies the authoritative data, safe failure, and measurement—not only the framework name.
Prompt engineering
Beginner — What makes a good prompt?
RAG
Beginner — What is RAG?
Embeddings
Beginner — Explain an embedding.
Vector databases
Beginner — Why use a vector index?
LangChain
Beginner — What does LangChain provide?
LangGraph
Beginner — What are nodes, edges, and state?
AI agents
Beginner — Agent versus chatbot?
MCP
Beginner — What problem does MCP solve?
Microsoft Agent Framework
Beginner — What is MAF?
Workflows
Beginner — Deterministic versus agentic workflow?
Memory
Beginner — Context versus memory?
AI security
Beginner — What is prompt injection?
Guardrails
Beginner — What is a guardrail?
Evaluation
Beginner — Why isn't normal unit testing enough?
Observability
Beginner — What do you trace in an AI request?
Cost optimization
Beginner — What determines AI cost?
Python
Beginner — Why learn Python as a MERN engineer?
Difficulty is an editorial estimate. Completion means you chose to mark a question as understood.